Proactive AI systems and robots are opposed to reactive AI
systems, which respond to explicit requests or external events. In
organizational psychology proactive behavior is understood as
anticipatory, self-initiated action [1]}. When
it comes to artificial agents, though, we lack a clear definition of
proactivity. Drawing inspiration from the human proactive process, we
can identify the functionalities that are needed for artificial
proactivity: context-awareness, activity recognition, goal reasoning,
planning, and plan execution and execution monitoring. Each one of
these functionalities in itself has been the subject of active
research [2]}, [3]}, [4]}, [5]}, [6]}. Proactivity needs
to contemplate these areas jointly and in a separate process to each.
Context-awareness is not the central topic in proactivity, but it is
used to understand what the current situation is, and with this
knowledge it is possible to decide how to act. Goal Reasoning (GR) deals
with questions about generating, selecting, maintaining and dispatching
goals for execution [4]}. Planning can be described as searching and selecting an optimal action
trajectory to a goal that is given externally by the human or by some
trigger. Proactivity resides on the abstraction level above. It is
finding the acting decisions or goals that should be planned for, hence,
it produces the input to a planner. Finally, plan execution and
monitoring are employed by Proactivity to enact the acting decision
inferred and to invoke new reasoning on proactivity when execution
fails.
